About Tennr:
Today, when you go to your doctor and need to be referred to a specialist (e.g., for sleep apnea), your doctor sends a fax (yes, in 2024, 90% of provider-provider communication is a 1980s fax). These are often converted into 20+ page PDFs, with handwritten (doctor’s handwriting!) notes, in thousands of different formats. The problem is so complex that a person has to read it, type it up, and manually enter your information. Tennr built RaeLM™ (7B—trained on 3M+ documents) to read these docs, talk to your doc to ensure nothing is missed, and text you to help schedule your appointment so you can get better, faster.
Tennr is a NYC-based tech company that launched out of Y-Combinator and is backed by Lightspeed Venture Partners, Andreessen Horowitz, Foundation Capital, The New Normal Fund, and other top investors.
Key Responsibilities
Machine Learning Engineers at Tennr are expected to wear a variety of hats. In the role, you will be expected to do the following:
- End-to-End Model Development: Architect, train, deploy, and monitor machine learning models, especially open-source LLMs and traditional computer vision models, to deliver tangible value to Tennr’s customers.
- Data Processing & ML Ops: Build, optimize, and maintain data pipelines and machine learning infrastructure, enhancing our operational capability as data volume and complexity grow.
- System Integration: Design and implement backend workflows leveraging machine learning to automate critical processes within Tennr’s platform.
- Custom Model Development: Fine-tune large language models, vision language models, and traditional computer vision models tailored specifically for medical document understanding and related tasks.
- Product Collaboration: Work closely with sales, customer success, and implementation teams to integrate customer feedback into actionable improvements and innovative solutions.
- Evaluation and Optimization: Develop robust evaluation frameworks to measure model performance and efficacy within complex, multi-model systems.
Qualifications
- 3+ years of professional experience in an applied machine learning engineering role, ideally post-BS/MS.
- Proven experience deploying and maintaining machine learning models in production environments, preferably within startups or rapidly evolving companies.
- Practical experience fine-tuning open-source LLMs and/or traditional computer vision models.
- Familiarity with robust ML Ops stacks, infrastructure management, and operational best practices for machine learning at scale.
- Comfort working within complex systems that integrate multiple machine learning components, with experience building evaluation frameworks to ensure system performance.
- Academic background in machine learning, mathematics, or related fields preferred.
- Up-to-date knowledge of recent advancements in large language models (LLMs) and enthusiasm for tracking rapid developments in the space.
- Prior startup experience or exposure to environments leveraging state-of-the-art ML models strongly preferred.
